Summary: Gambooza is seeking a Data Engineer & MLOps Engineer to own and scale the data and ML infrastructure for their computer vision systems, building systems from scratch and making key architectural decisions. Highlights: 1. Own critical data and ML infrastructure from an early stage 2. Work on real AI systems in production, directly impacting food waste 3. Shape the data and ML platform from the ground up **Company Description** Gambooza is a growing AI startup based in Madrid, building computer vision systems to help restaurants reduce food waste, tackle operational inefficiencies, and improve their long\-term viability. We’re tackling a massive, overlooked problem: inefficiencies and food waste in food service. Our technology brings visibility into kitchen operations using AI, helping operators reduce costs and environmental impact. We’re an early\-stage company, already backed by top programs like *Lanzadera, Madrid Food Innovation Hub, Basque Culinary Center*, and EU Tech Funds, and recognised in competitions such as the *Future Gastronomy Startup Competition* and *Premio Emprendimiento Digital* (Comunidad de Madrid). We’re now entering a scaling phase, moving from pilots to real deployments — and building the infrastructure to support it. You will join as a key early member of the tech team, working closely with the founders and acting as the second core technical profile, with ownership over data and ML infrastructure. We’re looking for someone with around 3\+ years of experience in data engineering, MLOps, or related roles, comfortable working in early\-stage environments and taking ownership end\-to\-end. If you want to work on real AI systems in production, own critical infrastructure, and help shape a company from the ground up, this is that kind of role. **Role** We’re looking for a Data Engineer \& MLOps Engineer to own and scale the data and ML infrastructure behind our platform. This is not a maintenance role — you’ll be building systems from scratch, making key architectural decisions, and working directly on production AI pipelines connected to real\-world environments (kitchens, cameras, edge devices). You will be responsible for everything that happens between raw data and reliable AI in production. **What you´ll do** * Design and build end\-to\-end data pipelines (from edge devices to cloud) * Own the infrastructure that powers our computer vision systems in production * Deploy, version, and monitor machine learning models at scale * Build robust MLOps workflows (training → evaluation → deployment → monitoring) * Ensure data quality, reliability, and observability across the platform * Optimize pipelines for performance, scalability, and cost * Work with large\-scale image data and real\-time ingestion systems * Support the integration and improvement of machine learning and computer vision models (data preparation, evaluation, and iteration loops) * Contribute to improving model performance in production through better data, monitoring, and feedback pipelines * Make foundational decisions on architecture, tooling, and infrastructure **What we are looking for** * Strong experience with Python and data\-intensive systems * Experience building and maintaining production data pipelines * Solid understanding of cloud infrastructure (GCP preferred, AWS also valid) * Hands\-on experience with Docker and production deployments * Familiarity with MLOps concepts (model lifecycle, monitoring, reproducibility) * Experience with workflow orchestration tools (Airflow, Prefect, or similar) * Strong engineering mindset: you care about reliability, scalability, and clean systems * Comfortable working in ambiguity and taking ownership of problems end\-to\-end **Strong Plus** * Experience deploying ML models in production * Experience with computer vision pipelines * Familiarity with Kubernetes or similar orchestration systems * Experience with tools like MLflow, Weights \& Biases, or feature stores * Experience working with streaming or near real\-time data systems **What makes this role differente?** * You’ll work on real AI systems in production, not experiments * Your work will directly impact how much food is wasted every day * You’ll have high ownership over critical infrastructure from early stage * You’ll help define how our data and ML platform is built from scratch * You’ll be part of a small, high\-impact team, where things move fast and ship often **Practical detailes \& Perks** * Full\-time role * Hybrid setup (Madrid, \~1\-2 days/week in office) * Spanish required * Flexible, outcome\-driven work environment (we care about results, not hours) * Free time to research on own projects * Competitive salary \+ phantom shares * High ownership and autonomy from day one * Flat organization with a small, highly talented team Tipo de puesto: Jornada completa, Contrato indefinido Sueldo: 30\.000,00€\-40\.000,00€ al año Beneficios: * Acciones empresariales * Ayuda al desarrollo profesional * Eventos sociales los viernes * Flexibilidad horaria * Ordenador de empresa * Teletrabajo opcional Posibilidad de trasladarse/mudarse: * 28231 Las Rozas de Madrid, Madrid provincia: Desplazarse al trabajo sin problemas o planificar mudarse antes de comenzar a trabajar (Obligatorio) Preguntas para la solicitud: * ¿Está usted legalmente autorizado para trabajar en España? * ¿Cuántos años de experiencia tienes utilizando Python para pipelines de datos o sistemas backend? * ¿Cuántos años de experiencia tienes construyendo y manteniendo pipelines de datos (ETL/ELT)? * ¿Tienes experiencia desplegando o manteniendo modelos de machine learning en producción? * ¿Has trabajado con herramientas de orquestación de workflows (Airflow, Prefect o similares)? * ¿Has trabajado con datos de imagen o pipelines de visión por computador? * ¿Has sido responsable de desplegar sistemas en producción end\-to\-end? * ¿Tienes experiencia práctica entrenando modelos de visión por computador (por ejemplo, clasificación, detección, segmentación)? * ¿Cuántos años de experiencia tienes en ingeniería de datos, MLOps o roles similares? * En una escala del 0 al 10, ¿cómo valorarías tu experiencia con Docker y la contenerización en entornos de producción? * From 1 to 10, how ready are you to leave the comfort of a stable job to build something meaningful in a high\-risk, high\-reward startup environment? (1\=“I need full security”, 10\=“I thrive in chaos") Educación: * Licenciatura/Grado (Deseable) Experiencia: * Data science: 2 años (Obligatorio) Idioma: * Español (Obligatorio) Ubicación del trabajo: Teletrabajo híbrido en 28231 Las Rozas de Madrid, Madrid provincia